text: 
  #mnist experiment :QvMXCpIy
  
  ######### Training code ##################################################
  import xgboost as xgb
  app_name="mlvajra_release"
  model_name="vajra-model"
  
  
  train = xgb.DMatrix(get_test_point(), label=[0])
  # We then create parameters, watchlist, and specify the number of rounds
  # This is code that we use to build our XGBoost Model, and your code may differ.
  param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
  watchlist = [(dtrain, 'train')]
  num_round = 2
  bst = xgb.train(param, dtrain, num_round, watchlist)
  
  def predict(xs):
      return bst.predict(xgb.DMatrix(xs))
  
  ##########################################################################
  
  
  ############################ DEPLOY ############################################
  from mlvajra.deploy.clipper import mlDeploy
  deploy=mlDeploy()
  deploy.create_application(app_name,"integers","vajra-default")
  deploy.deploy_model(model_name,predict,0.1,pkgs_to_install=["xgboost"],model_type='PYTHON')
  deploy.conn.link_model_to_app(app_name,model_name)
  
  ###################################################################################
  
  
  ############################ inference time ###########################################
  
  def get_test_point():
      return [np.random.randint(255) for _ in range(784)]
  
  import requests, json
  # Get Address
  addr = deploy.conn.get_query_addr()
  # Post Query
  response = requests.post(
       "http://%s/%s/predict" % (addr, 'xgboost-test'),
       headers={"Content-type": "application/json"},
       data=json.dumps({
           'input': get_test_point()
       }))
  result = response.json()
  if response.status_code == requests.codes.ok and result["default"]:
       print('A default prediction was returned.')
  elif response.status_code != requests.codes.ok:
      print(result)
      raise BenchmarkException(response.text)
  else:
      print('Prediction Returned:', result)
  ###############################################################################################
  deploy.conn.stop_all()
  
  
  